Description
Car data has 105 observations and 12 variables. All variables except the 12th are standardized such that mean of each of them is 0 and standard deviation is 1. First 10 variables are various characteristics of the cars. The 11th variable y is the price. The 12th variable is a binary variable.
Format
A data frame with 105 observations on the following 12 variables.
Weight- weights of the cars
Length- overall length
Wheel.base- length of wheelbase
Width- width of car
Frt.Leg.Room- maximum front leg room
Front.Hd- distance between the car's head-liner and the head of a 5 ft. 9 in. front seat passenger
Turning- the radius of the turning circle
Disp- engine displacement
HP- net horsepower
Tank- fuel refill capacity
y- price
y1- High or low price
Source
Terry Therneau, Beth Atkinson and Brian Ripley (2014). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-8. http://CRAN.R-project.org/package=rpartDetails
The data is created from car90 data of rpart package with selected 11 variables. The selected variables are Weight,Length,Wheel.base,Width,Frt.Leg.Room,Front.Hd,Turning,Disp,HP,Tank,Price. All these variables are standardized such that each of them has mean 0 and standard deviation 1. Price variable has been renamed as y. The variable y1 is a dichotomous variable created from that the data such that if price >=25000, then y1=1 else y1=0. Only complete cases are considered, so the data has 105 observations in place of 111 observations in car90 data set.